Multi-disease detection system with X-ray images using deep learning techniques

In the realm of medical diagnostics, advanced deep learning algorithms have become powerful tools for detecting and diagnosing diseases. This study introduces a groundbreaking Multi-Disease Detection System designed specifically for analyzing X-ray images. It focuses on detecting Alzheimer's disease, brain tumors, COVID-19 infection, and pneumonia, marking a significant advancement in medical imaging analysis and clinical decision-making. The system utilizes CNN and RNN to attain unparalleled accuracy and reliability in disease detection. By processing MRI and CT scans for Alzheimer's and brain tumor detection, and chest X-ray or CT images for COVID-19 and pneumonia detection, the framework identifies key features indicative of disease pathology. Through careful image preprocessing, noise is minimized, and features are enhanced to optimize CNN performance. Subsequently, RNNs analyze the temporal sequences of features, particularly crucial for diseases like COVID-19 with rapidly evolving symptoms. The system is trained and evaluated on a diverse dataset encompassing various disease stages and manifestations, demonstrating superior accuracy, sensitivity, specificity, and AUC-ROC metrics. This Multi-Disease Detection System represents a significant breakthrough in medical imaging, to take out healthcare specialists with a robust device for timely and accurate disease detection. By leveraging deep learning techniques, it not only improves patient outcomes but also transforms healthcare delivery, ushering in an era where early disease detection is commonplace.